fix(approx_fns): use exact percentile when no compression

[aryan-212]

The interpolation step assumes centroids represent clusters of multiple points. But if the number of input rows is small (≤ the digest's max_size / compression threshold), no compression ever happens: every centroid has weight 1 and corresponds to exactly one input value.

In that regime, interpolation is not just unnecessary — it is actively wrong. The t-digest interpolates between adjacent centroids based on where the rank falls inside the centroid's weight, using half-deltas to neighbors. When every centroid has weight 1, this produces values that drift away from any actual data point.

This is particularly surprising for users running small queries or unit tests — they expect percentile functions on a handful of values to return one of those values.

(used gpt to frame this a bit properly)

Concrete Example

Lets take a small example from the TPCDS Schema

select cc_sq_ft from call_center;

none	cc_sq_ft
1	6144
2	6144
3	19345
4	21156
5	21156
6	22743
7	34643
8	42935
9	52514
10	65772
11	76815
12	84336
13	105138
14	119886

Now if we take a small APPROX_PERCENTILE query like:-

select approx_percentile(cc_sq_ft,0.85) from call_center limit 50

From here, 0.85*14 yields 11.9 or 12 so the output for the above APPROX_PERCENITLE query should be 84336 and that is what we get when we run the same query in Databricks

But in Datafusion this comes up as

This PR aims to fix this.